[inputcoffee]

As others are pointing out, TF isn't that hard.

Or, rather, it is hard but the difficulty is from getting an intuition for what part of this weird multi layer net is producing this weird behavior and is it an artefact or something interesting, and is the connectivity complete and is should I change the learning rate and activation functions?

The real reason to use Tensorflow is the same reason you might use a Go framework instead of Rails: in your heart you have this hope that this thing will one day grow into a really large project and support lots of people and that will be easier with this scalable, optimized code.

Its not even that you'll hit Google scale, its that you'll hit popular scale and still serve the whole thing out of your Digital Ocean droplet.

[rahimnathwani]

"Its not even that you'll hit Google scale, its that you'll hit popular scale and still serve the whole thing out of your Digital Ocean droplet."

Are you saying that model inference is slower or less efficient for a model built and trained in Keras, than the same model architecture built directly in tensorflow?

[inputcoffee]

Actually, with Tensorflow as a Keras backend, I would expect them to be the same. I am not sure where the performance difference between TF and TF as a backend come from.

I do think that pure TF would be easier to scale up over multiple servers etc. but that's only because I don't know how it would work in Keras. Maybe its easy.

[eanzenberg]

Its pretty straightforward to convert a keras model to a tf graph, as long as you used a tf backend in keras.

[crucialfelix]

I would think the difference would be from the data input pipeline, efficiency in batching, updating online models. The inference itself would be the exact same.

[robertAngst]

So I'm automating my job on a shitty tiny laptop.

Do you think I'll be able to use DL?